Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations2589083
Missing cells11664492
Missing cells (%)28.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory740.7 MiB
Average record size in memory300.0 B

Variable types

Text1
DateTime1
Numeric13
Categorical1

Alerts

AQI is highly overall correlated with PM10 and 1 other fieldsHigh correlation
Benzene is highly overall correlated with Toluene and 1 other fieldsHigh correlation
NO is highly overall correlated with NOxHigh correlation
NO2 is highly overall correlated with NOxHigh correlation
NOx is highly overall correlated with NO and 1 other fieldsHigh correlation
PM10 is highly overall correlated with AQI and 1 other fieldsHigh correlation
PM2.5 is highly overall correlated with AQI and 1 other fieldsHigh correlation
Toluene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
Xylene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
PM2.5 has 647689 (25.0%) missing values Missing
PM10 has 1119252 (43.2%) missing values Missing
NO has 553711 (21.4%) missing values Missing
NO2 has 528973 (20.4%) missing values Missing
NOx has 490808 (19.0%) missing values Missing
NH3 has 1236618 (47.8%) missing values Missing
CO has 499302 (19.3%) missing values Missing
SO2 has 742737 (28.7%) missing values Missing
O3 has 725973 (28.0%) missing values Missing
Benzene has 861579 (33.3%) missing values Missing
Toluene has 1042366 (40.3%) missing values Missing
Xylene has 2075104 (80.1%) missing values Missing
AQI has 570190 (22.0%) missing values Missing
AQI_Bucket has 570190 (22.0%) missing values Missing
CO is highly skewed (γ1 = 33.50808698) Skewed
Benzene is highly skewed (γ1 = 21.34590047) Skewed
NOx has 117924 (4.6%) zeros Zeros
CO has 181502 (7.0%) zeros Zeros
Benzene has 392342 (15.2%) zeros Zeros
Toluene has 312987 (12.1%) zeros Zeros
Xylene has 208359 (8.0%) zeros Zeros

Reproduction

Analysis started2025-01-05 00:40:35.434698
Analysis finished2025-01-05 00:41:32.057904
Duration56.62 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct110
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.1 MiB
2025-01-04T18:41:32.161255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12945415
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAP001
2nd rowAP001
3rd rowAP001
4th rowAP001
5th rowAP001
ValueCountFrequency (%)
ka003 48192
 
1.9%
dl013 48192
 
1.9%
tn004 48192
 
1.9%
tn003 48192
 
1.9%
tn001 48192
 
1.9%
up012 48192
 
1.9%
up014 48192
 
1.9%
dl007 48192
 
1.9%
dl008 48192
 
1.9%
gj001 48192
 
1.9%
Other values (100) 2107163
81.4%
2025-01-04T18:41:32.343529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4104970
31.7%
1 1161659
 
9.0%
L 1132467
 
8.7%
D 1132017
 
8.7%
3 556792
 
4.3%
2 524441
 
4.1%
T 372708
 
2.9%
A 357403
 
2.8%
4 350757
 
2.7%
K 325241
 
2.5%
Other values (19) 2926960
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12945415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4104970
31.7%
1 1161659
 
9.0%
L 1132467
 
8.7%
D 1132017
 
8.7%
3 556792
 
4.3%
2 524441
 
4.1%
T 372708
 
2.9%
A 357403
 
2.8%
4 350757
 
2.7%
K 325241
 
2.5%
Other values (19) 2926960
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12945415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4104970
31.7%
1 1161659
 
9.0%
L 1132467
 
8.7%
D 1132017
 
8.7%
3 556792
 
4.3%
2 524441
 
4.1%
T 372708
 
2.9%
A 357403
 
2.8%
4 350757
 
2.7%
K 325241
 
2.5%
Other values (19) 2926960
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12945415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4104970
31.7%
1 1161659
 
9.0%
L 1132467
 
8.7%
D 1132017
 
8.7%
3 556792
 
4.3%
2 524441
 
4.1%
T 372708
 
2.9%
A 357403
 
2.8%
4 350757
 
2.7%
K 325241
 
2.5%
Other values (19) 2926960
22.6%
Distinct48192
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size19.8 MiB
Minimum2015-01-01 01:00:00
Maximum2020-07-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-04T18:41:32.407979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:32.477047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PM2.5
Real number (ℝ)

High correlation  Missing 

Distinct45086
Distinct (%)2.3%
Missing647689
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean80.864814
Minimum0.01
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:32.547150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile10
Q128.16
median52.59
Q397.74
95-th percentile251.29
Maximum1000
Range999.99
Interquartile range (IQR)69.58

Descriptive statistics

Standard deviation89.476181
Coefficient of variation (CV)1.1064909
Kurtosis18.305458
Mean80.864814
Median Absolute Deviation (MAD)29.59
Skewness3.3444117
Sum1.5699046 × 108
Variance8005.987
MonotonicityNot monotonic
2025-01-04T18:41:32.613073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 4989
 
0.2%
27 4970
 
0.2%
33 4793
 
0.2%
56 4782
 
0.2%
28 4717
 
0.2%
39 4697
 
0.2%
29 4684
 
0.2%
40 4649
 
0.2%
38 4629
 
0.2%
25 4537
 
0.2%
Other values (45076) 1893947
73.2%
(Missing) 647689
 
25.0%
ValueCountFrequency (%)
0.01 39
< 0.1%
0.02 55
< 0.1%
0.03 68
< 0.1%
0.04 56
< 0.1%
0.05 56
< 0.1%
0.06 39
< 0.1%
0.07 45
< 0.1%
0.08 44
< 0.1%
0.09 40
< 0.1%
0.1 63
< 0.1%
ValueCountFrequency (%)
1000 182
 
< 0.1%
999.99 478
< 0.1%
999.83 1
 
< 0.1%
999.75 2
 
< 0.1%
999.5 2
 
< 0.1%
999.25 1
 
< 0.1%
998.75 1
 
< 0.1%
998.09 1
 
< 0.1%
998 1
 
< 0.1%
997.93 1
 
< 0.1%

PM10
Real number (ℝ)

High correlation  Missing 

Distinct56960
Distinct (%)3.9%
Missing1119252
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean158.48391
Minimum0.01
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:32.678646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile25
Q164
median116.25
Q3204
95-th percentile440.75
Maximum1000
Range999.99
Interquartile range (IQR)140

Descriptive statistics

Standard deviation139.78826
Coefficient of variation (CV)0.8820344
Kurtosis5.5620304
Mean158.48391
Median Absolute Deviation (MAD)62.25
Skewness2.0478487
Sum2.3294456 × 108
Variance19540.757
MonotonicityNot monotonic
2025-01-04T18:41:32.746281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 4818
 
0.2%
56 1880
 
0.1%
69 1827
 
0.1%
59 1821
 
0.1%
53 1816
 
0.1%
80 1804
 
0.1%
64 1779
 
0.1%
50 1774
 
0.1%
86 1770
 
0.1%
72 1758
 
0.1%
Other values (56950) 1448784
56.0%
(Missing) 1119252
43.2%
ValueCountFrequency (%)
0.01 25
< 0.1%
0.02 30
< 0.1%
0.03 45
< 0.1%
0.04 35
< 0.1%
0.05 36
< 0.1%
0.06 49
< 0.1%
0.07 32
< 0.1%
0.08 44
< 0.1%
0.09 37
< 0.1%
0.1 47
< 0.1%
ValueCountFrequency (%)
1000 189
< 0.1%
999.99 236
< 0.1%
999.96 1
 
< 0.1%
999.83 1
 
< 0.1%
999.75 3
 
< 0.1%
999.33 1
 
< 0.1%
999.16 1
 
< 0.1%
999 2
 
< 0.1%
998.9 1
 
< 0.1%
998.58 1
 
< 0.1%

NO
Real number (ℝ)

High correlation  Missing 

Distinct36985
Distinct (%)1.8%
Missing553711
Missing (%)21.4%
Infinite0
Infinite (%)0.0%
Mean22.788253
Minimum0.01
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:32.811316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.88
Q13.05
median7.15
Q318.58
95-th percentile103.04
Maximum500
Range499.99
Interquartile range (IQR)15.53

Descriptive statistics

Standard deviation48.461463
Coefficient of variation (CV)2.1265984
Kurtosis28.356179
Mean22.788253
Median Absolute Deviation (MAD)5.04
Skewness4.7654233
Sum46382573
Variance2348.5134
MonotonicityNot monotonic
2025-01-04T18:41:32.878867image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4452
 
0.2%
3 4151
 
0.2%
1.5 4072
 
0.2%
0.8 3966
 
0.2%
2.1 3930
 
0.2%
2 3922
 
0.2%
2.5 3839
 
0.1%
1.1 3825
 
0.1%
1.4 3813
 
0.1%
0.9 3805
 
0.1%
Other values (36975) 1995597
77.1%
(Missing) 553711
 
21.4%
ValueCountFrequency (%)
0.01 189
 
< 0.1%
0.02 249
 
< 0.1%
0.03 268
 
< 0.1%
0.04 222
 
< 0.1%
0.05 230
 
< 0.1%
0.06 203
 
< 0.1%
0.07 253
 
< 0.1%
0.08 277
 
< 0.1%
0.09 191
 
< 0.1%
0.1 1447
0.1%
ValueCountFrequency (%)
500 1
 
< 0.1%
499.99 2
< 0.1%
499.9 1
 
< 0.1%
499.8 3
< 0.1%
499.76 2
< 0.1%
499.74 1
 
< 0.1%
499.7 3
< 0.1%
499.67 1
 
< 0.1%
499.65 1
 
< 0.1%
499.63 1
 
< 0.1%

NO2
Real number (ℝ)

High correlation  Missing 

Distinct26703
Distinct (%)1.3%
Missing528973
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean35.236891
Minimum0.01
Maximum499.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:32.944042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile4.33
Q113.1
median24.79
Q345.48
95-th percentile100.3
Maximum499.99
Range499.98
Interquartile range (IQR)32.38

Descriptive statistics

Standard deviation34.975083
Coefficient of variation (CV)0.99257004
Kurtosis17.480945
Mean35.236891
Median Absolute Deviation (MAD)14.01
Skewness3.0633094
Sum72591872
Variance1223.2564
MonotonicityNot monotonic
2025-01-04T18:41:33.059857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 1210
 
< 0.1%
7 1171
 
< 0.1%
0.2 1147
 
< 0.1%
8 1119
 
< 0.1%
15 1109
 
< 0.1%
10 1030
 
< 0.1%
14 1021
 
< 0.1%
12 991
 
< 0.1%
14.5 988
 
< 0.1%
11 981
 
< 0.1%
Other values (26693) 2049343
79.2%
(Missing) 528973
 
20.4%
ValueCountFrequency (%)
0.01 180
 
< 0.1%
0.02 313
< 0.1%
0.03 358
< 0.1%
0.04 310
< 0.1%
0.05 258
 
< 0.1%
0.06 265
 
< 0.1%
0.07 281
< 0.1%
0.08 246
 
< 0.1%
0.09 199
 
< 0.1%
0.1 678
< 0.1%
ValueCountFrequency (%)
499.99 2
< 0.1%
499.97 1
< 0.1%
499.9 1
< 0.1%
499.8 1
< 0.1%
499.51 1
< 0.1%
499.28 1
< 0.1%
498.96 1
< 0.1%
498.4 1
< 0.1%
498.3 1
< 0.1%
498.15 1
< 0.1%

NOx
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct40690
Distinct (%)1.9%
Missing490808
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean40.551148
Minimum0
Maximum500
Zeros117924
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:33.124254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.35
median22.86
Q345.7
95-th percentile143.35
Maximum500
Range500
Interquartile range (IQR)34.35

Descriptive statistics

Standard deviation55.908938
Coefficient of variation (CV)1.3787264
Kurtosis17.000603
Mean40.551148
Median Absolute Deviation (MAD)14.23
Skewness3.6031296
Sum85087461
Variance3125.8094
MonotonicityNot monotonic
2025-01-04T18:41:33.191661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117924
 
4.6%
6.24 13528
 
0.5%
2.21 11571
 
0.4%
5 1349
 
0.1%
4.9 1216
 
< 0.1%
7 1172
 
< 0.1%
10 1161
 
< 0.1%
9 1155
 
< 0.1%
11 1105
 
< 0.1%
11.5 1065
 
< 0.1%
Other values (40680) 1947029
75.2%
(Missing) 490808
 
19.0%
ValueCountFrequency (%)
0 117924
4.6%
0.01 87
 
< 0.1%
0.02 82
 
< 0.1%
0.03 250
 
< 0.1%
0.04 92
 
< 0.1%
0.05 194
 
< 0.1%
0.06 42
 
< 0.1%
0.07 74
 
< 0.1%
0.08 124
 
< 0.1%
0.09 56
 
< 0.1%
ValueCountFrequency (%)
500 4
< 0.1%
499.99 2
< 0.1%
499.97 1
 
< 0.1%
499.95 1
 
< 0.1%
499.94 1
 
< 0.1%
499.9 2
< 0.1%
499.87 1
 
< 0.1%
499.82 1
 
< 0.1%
499.8 2
< 0.1%
499.73 1
 
< 0.1%

NH3
Real number (ℝ)

Missing 

Distinct20523
Distinct (%)1.5%
Missing1236618
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean28.708556
Minimum0.01
Maximum499.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:33.255977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3.53
Q111.23
median22.35
Q337.78
95-th percentile73.57
Maximum499.97
Range499.96
Interquartile range (IQR)26.55

Descriptive statistics

Standard deviation27.532443
Coefficient of variation (CV)0.95903266
Kurtosis34.330607
Mean28.708556
Median Absolute Deviation (MAD)12.38
Skewness4.0288524
Sum38827317
Variance758.03539
MonotonicityNot monotonic
2025-01-04T18:41:33.324389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1494
 
0.1%
9 1303
 
0.1%
14.62 988
 
< 0.1%
7 937
 
< 0.1%
5 910
 
< 0.1%
26 869
 
< 0.1%
10.3 855
 
< 0.1%
10.5 855
 
< 0.1%
0.1 854
 
< 0.1%
12 845
 
< 0.1%
Other values (20513) 1342555
51.9%
(Missing) 1236618
47.8%
ValueCountFrequency (%)
0.01 131
 
< 0.1%
0.02 114
 
< 0.1%
0.03 149
 
< 0.1%
0.04 101
 
< 0.1%
0.05 157
 
< 0.1%
0.06 67
 
< 0.1%
0.07 79
 
< 0.1%
0.08 150
 
< 0.1%
0.09 49
 
< 0.1%
0.1 854
< 0.1%
ValueCountFrequency (%)
499.97 1
< 0.1%
499.56 1
< 0.1%
499.12 1
< 0.1%
498.54 1
< 0.1%
497.99 1
< 0.1%
497.95 1
< 0.1%
497.88 1
< 0.1%
497.38 1
< 0.1%
495.23 1
< 0.1%
494.11 1
< 0.1%

CO
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct7263
Distinct (%)0.3%
Missing499302
Missing (%)19.3%
Infinite0
Infinite (%)0.0%
Mean1.502366
Minimum0
Maximum498.57
Zeros181502
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:33.391623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.41
median0.8
Q31.38
95-th percentile3.62
Maximum498.57
Range498.57
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation6.2924451
Coefficient of variation (CV)4.1883569
Kurtosis1632.1903
Mean1.502366
Median Absolute Deviation (MAD)0.45
Skewness33.508087
Sum3139616
Variance39.594866
MonotonicityNot monotonic
2025-01-04T18:41:33.459027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 181502
 
7.0%
0.7 21885
 
0.8%
0.6 21096
 
0.8%
0.8 21087
 
0.8%
0.4 20810
 
0.8%
0.5 20721
 
0.8%
0.3 17503
 
0.7%
0.55 16393
 
0.6%
0.72 16217
 
0.6%
0.65 16175
 
0.6%
Other values (7253) 1736392
67.1%
(Missing) 499302
 
19.3%
ValueCountFrequency (%)
0 181502
7.0%
0.01 4301
 
0.2%
0.02 3905
 
0.2%
0.03 2644
 
0.1%
0.04 2400
 
0.1%
0.05 2661
 
0.1%
0.06 2393
 
0.1%
0.07 2668
 
0.1%
0.08 3198
 
0.1%
0.09 2654
 
0.1%
ValueCountFrequency (%)
498.57 1
< 0.1%
494.9 1
< 0.1%
490.35 1
< 0.1%
485.73 1
< 0.1%
483.37 1
< 0.1%
476.2 1
< 0.1%
476.02 1
< 0.1%
475.4 1
< 0.1%
473.81 1
< 0.1%
470.72 1
< 0.1%

SO2
Real number (ℝ)

Missing 

Distinct15385
Distinct (%)0.8%
Missing742737
Missing (%)28.7%
Infinite0
Infinite (%)0.0%
Mean12.116025
Minimum0.01
Maximum199.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:33.525083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.38
Q14.25
median8.25
Q314.53
95-th percentile34.85
Maximum199.96
Range199.95
Interquartile range (IQR)10.28

Descriptive statistics

Standard deviation14.673848
Coefficient of variation (CV)1.2111108
Kurtosis36.042377
Mean12.116025
Median Absolute Deviation (MAD)4.65
Skewness4.7860374
Sum22370373
Variance215.32181
MonotonicityNot monotonic
2025-01-04T18:41:33.595809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 3306
 
0.1%
4 3090
 
0.1%
2.5 2998
 
0.1%
2 2920
 
0.1%
5 2779
 
0.1%
3.5 2728
 
0.1%
5.5 2623
 
0.1%
4.5 2544
 
0.1%
6 2531
 
0.1%
7 2522
 
0.1%
Other values (15375) 1818305
70.2%
(Missing) 742737
28.7%
ValueCountFrequency (%)
0.01 366
 
< 0.1%
0.02 464
 
< 0.1%
0.03 510
 
< 0.1%
0.04 474
 
< 0.1%
0.05 388
 
< 0.1%
0.06 410
 
< 0.1%
0.07 363
 
< 0.1%
0.08 395
 
< 0.1%
0.09 316
 
< 0.1%
0.1 2303
0.1%
ValueCountFrequency (%)
199.96 2
< 0.1%
199.95 1
< 0.1%
199.93 1
< 0.1%
199.9 1
< 0.1%
199.85 1
< 0.1%
199.81 1
< 0.1%
199.77 1
< 0.1%
199.75 1
< 0.1%
199.72 1
< 0.1%
199.7 1
< 0.1%

O3
Real number (ℝ)

Missing 

Distinct23226
Distinct (%)1.2%
Missing725973
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean38.064085
Minimum0.01
Maximum997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:33.667315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3
Q111.02
median24.75
Q349.53
95-th percentile111.6
Maximum997
Range996.99
Interquartile range (IQR)38.51

Descriptive statistics

Standard deviation47.106533
Coefficient of variation (CV)1.2375585
Kurtosis57.530379
Mean38.064085
Median Absolute Deviation (MAD)16.49
Skewness5.4743607
Sum70917577
Variance2219.0254
MonotonicityNot monotonic
2025-01-04T18:41:33.733901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 1495
 
0.1%
3 1364
 
0.1%
2 1330
 
0.1%
4 1237
 
< 0.1%
7 1155
 
< 0.1%
3.5 1152
 
< 0.1%
1 1152
 
< 0.1%
4.5 1122
 
< 0.1%
5.5 1114
 
< 0.1%
17 1087
 
< 0.1%
Other values (23216) 1850902
71.5%
(Missing) 725973
 
28.0%
ValueCountFrequency (%)
0.01 195
 
< 0.1%
0.02 213
 
< 0.1%
0.03 184
 
< 0.1%
0.04 136
 
< 0.1%
0.05 119
 
< 0.1%
0.06 127
 
< 0.1%
0.07 145
 
< 0.1%
0.08 124
 
< 0.1%
0.09 105
 
< 0.1%
0.1 636
< 0.1%
ValueCountFrequency (%)
997 1
< 0.1%
996 2
< 0.1%
992 1
< 0.1%
989 1
< 0.1%
988.17 1
< 0.1%
984.33 1
< 0.1%
982 2
< 0.1%
980 1
< 0.1%
977 1
< 0.1%
975 1
< 0.1%

Benzene
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct11356
Distinct (%)0.7%
Missing861579
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean3.3054932
Minimum0
Maximum498.07
Zeros392342
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:33.797582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.96
Q33.23
95-th percentile11.8
Maximum498.07
Range498.07
Interquartile range (IQR)3.15

Descriptive statistics

Standard deviation12.140528
Coefficient of variation (CV)3.6728341
Kurtosis662.1786
Mean3.3054932
Median Absolute Deviation (MAD)0.96
Skewness21.3459
Sum5710252.7
Variance147.39242
MonotonicityNot monotonic
2025-01-04T18:41:33.865800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 392342
 
15.2%
0.1 44769
 
1.7%
0.2 23687
 
0.9%
0.3 16939
 
0.7%
0.4 14282
 
0.6%
0.35 9732
 
0.4%
0.23 9314
 
0.4%
0.15 8765
 
0.3%
0.45 8682
 
0.3%
0.7 8602
 
0.3%
Other values (11346) 1190390
46.0%
(Missing) 861579
33.3%
ValueCountFrequency (%)
0 392342
15.2%
0.01 6150
 
0.2%
0.02 4273
 
0.2%
0.03 7334
 
0.3%
0.04 4581
 
0.2%
0.05 6631
 
0.3%
0.06 3142
 
0.1%
0.07 3564
 
0.1%
0.08 6076
 
0.2%
0.09 3149
 
0.1%
ValueCountFrequency (%)
498.07 4
< 0.1%
491.51 8
< 0.1%
490 1
 
< 0.1%
488.48 1
 
< 0.1%
487.79 1
 
< 0.1%
487.6 1
 
< 0.1%
487.21 1
 
< 0.1%
487.2 1
 
< 0.1%
486.58 1
 
< 0.1%
485.69 1
 
< 0.1%

Toluene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct24266
Distinct (%)1.6%
Missing1042366
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean14.902663
Minimum0
Maximum499.99
Zeros312987
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:33.931760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.34
median3.4
Q315.1
95-th percentile64.35
Maximum499.99
Range499.99
Interquartile range (IQR)14.76

Descriptive statistics

Standard deviation33.297295
Coefficient of variation (CV)2.2343184
Kurtosis49.998524
Mean14.902663
Median Absolute Deviation (MAD)3.4
Skewness5.8398176
Sum23050203
Variance1108.7098
MonotonicityNot monotonic
2025-01-04T18:41:34.053641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 312987
 
12.1%
0.1 7138
 
0.3%
0.2 6966
 
0.3%
0.3 5665
 
0.2%
1.1 5423
 
0.2%
0.5 5136
 
0.2%
0.6 4530
 
0.2%
0.8 4172
 
0.2%
0.9 4073
 
0.2%
0.4 3690
 
0.1%
Other values (24256) 1186937
45.8%
(Missing) 1042366
40.3%
ValueCountFrequency (%)
0 312987
12.1%
0.01 1297
 
0.1%
0.02 1735
 
0.1%
0.03 2474
 
0.1%
0.04 1682
 
0.1%
0.05 2305
 
0.1%
0.06 2359
 
0.1%
0.07 2030
 
0.1%
0.08 2292
 
0.1%
0.09 1353
 
0.1%
ValueCountFrequency (%)
499.99 1
< 0.1%
499.8 1
< 0.1%
499.5 2
< 0.1%
499.4 1
< 0.1%
499.2 1
< 0.1%
499.08 1
< 0.1%
499.05 1
< 0.1%
498.9 1
< 0.1%
498.8 1
< 0.1%
498.6 1
< 0.1%

Xylene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct5829
Distinct (%)1.1%
Missing2075104
Missing (%)80.1%
Infinite0
Infinite (%)0.0%
Mean2.4488809
Minimum0
Maximum499.99
Zeros208359
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:34.120345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q31.83
95-th percentile10.82
Maximum499.99
Range499.99
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation8.9734698
Coefficient of variation (CV)3.6643145
Kurtosis444.26888
Mean2.4488809
Median Absolute Deviation (MAD)0.2
Skewness16.347887
Sum1258673.4
Variance80.523161
MonotonicityNot monotonic
2025-01-04T18:41:34.190022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 208359
 
8.0%
0.1 11522
 
0.4%
2 6184
 
0.2%
0.65 5635
 
0.2%
0.2 4107
 
0.2%
0.3 3815
 
0.1%
0.03 3739
 
0.1%
0.05 3618
 
0.1%
0.4 3460
 
0.1%
0.15 3030
 
0.1%
Other values (5819) 260510
 
10.1%
(Missing) 2075104
80.1%
ValueCountFrequency (%)
0 208359
8.0%
0.01 1971
 
0.1%
0.02 1546
 
0.1%
0.03 3739
 
0.1%
0.04 1815
 
0.1%
0.05 3618
 
0.1%
0.06 1202
 
< 0.1%
0.07 1559
 
0.1%
0.08 3010
 
0.1%
0.09 1067
 
< 0.1%
ValueCountFrequency (%)
499.99 1
< 0.1%
476.31 1
< 0.1%
461.39 1
< 0.1%
433.94 1
< 0.1%
423.48 1
< 0.1%
422.86 1
< 0.1%
419.88 1
< 0.1%
406.29 1
< 0.1%
402.64 1
< 0.1%
398.9 1
< 0.1%

AQI
Real number (ℝ)

High correlation  Missing 

Distinct1601
Distinct (%)0.1%
Missing570190
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean180.17303
Minimum5
Maximum3133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 MiB
2025-01-04T18:41:34.254374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile44
Q184
median131
Q3259
95-th percentile416
Maximum3133
Range3128
Interquartile range (IQR)175

Descriptive statistics

Standard deviation140.40953
Coefficient of variation (CV)0.77930384
Kurtosis38.303665
Mean180.17303
Median Absolute Deviation (MAD)63
Skewness3.4454512
Sum3.6375006 × 108
Variance19714.837
MonotonicityNot monotonic
2025-01-04T18:41:34.323812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 16729
 
0.6%
104 15664
 
0.6%
101 15486
 
0.6%
106 14777
 
0.6%
100 14516
 
0.6%
103 14311
 
0.6%
105 13743
 
0.5%
108 12742
 
0.5%
88 12630
 
0.5%
64 12518
 
0.5%
Other values (1591) 1875777
72.4%
(Missing) 570190
 
22.0%
ValueCountFrequency (%)
5 4
 
< 0.1%
6 34
 
< 0.1%
7 64
 
< 0.1%
8 27
 
< 0.1%
9 62
 
< 0.1%
10 112
< 0.1%
11 102
< 0.1%
12 126
< 0.1%
13 128
< 0.1%
14 160
< 0.1%
ValueCountFrequency (%)
3133 8
< 0.1%
3111 8
< 0.1%
3084 8
< 0.1%
3057 8
< 0.1%
3043 8
< 0.1%
3001 8
< 0.1%
3000 8
< 0.1%
2996 8
< 0.1%
2987 8
< 0.1%
2969 8
< 0.1%

AQI_Bucket
Categorical

Missing 

Distinct6
Distinct (%)< 0.1%
Missing570190
Missing (%)22.0%
Memory size156.2 MiB
Moderate
675008 
Satisfactory
530164 
Very Poor
301150 
Poor
239990 
Good
152113 

Length

Max length12
Median length9
Mean length8.3033633
Min length4

Characters and Unicode

Total characters16763602
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowModerate
3rd rowModerate
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Moderate 675008
26.1%
Satisfactory 530164
20.5%
Very Poor 301150
11.6%
Poor 239990
 
9.3%
Good 152113
 
5.9%
Severe 120468
 
4.7%
(Missing) 570190
22.0%

Length

2025-01-04T18:41:34.397838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-04T18:41:34.460841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
moderate 675008
29.1%
poor 541140
23.3%
satisfactory 530164
22.9%
very 301150
13.0%
good 152113
 
6.6%
severe 120468
 
5.2%

Most occurring characters

ValueCountFrequency (%)
o 2591678
15.5%
r 2167930
12.9%
e 2012570
12.0%
a 1735336
10.4%
t 1735336
10.4%
y 831314
 
5.0%
d 827121
 
4.9%
M 675008
 
4.0%
S 650632
 
3.9%
P 541140
 
3.2%
Other values (8) 2995537
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16763602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2591678
15.5%
r 2167930
12.9%
e 2012570
12.0%
a 1735336
10.4%
t 1735336
10.4%
y 831314
 
5.0%
d 827121
 
4.9%
M 675008
 
4.0%
S 650632
 
3.9%
P 541140
 
3.2%
Other values (8) 2995537
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16763602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2591678
15.5%
r 2167930
12.9%
e 2012570
12.0%
a 1735336
10.4%
t 1735336
10.4%
y 831314
 
5.0%
d 827121
 
4.9%
M 675008
 
4.0%
S 650632
 
3.9%
P 541140
 
3.2%
Other values (8) 2995537
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16763602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2591678
15.5%
r 2167930
12.9%
e 2012570
12.0%
a 1735336
10.4%
t 1735336
10.4%
y 831314
 
5.0%
d 827121
 
4.9%
M 675008
 
4.0%
S 650632
 
3.9%
P 541140
 
3.2%
Other values (8) 2995537
17.9%

Interactions

2025-01-04T18:41:24.446556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:05.756434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:07.408987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:08.909008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:10.662152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:12.381205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:14.127813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:15.524354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:17.169777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:18.841366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:20.439184image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:21.959565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.360406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:24.568768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:05.876340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:07.531016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:09.099212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:10.781008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:12.506141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:14.233881image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:15.638637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:17.284148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:18.957767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:20.541904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:22.057712image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.427734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:24.726821image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:06.018852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:07.650394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:09.258646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:10.929586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:12.651870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:14.345910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:15.772113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:17.422774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:19.092304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:20.710565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:22.174350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.502459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:24.873626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:06.162157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:07.770313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:09.401093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:11.097432image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:12.793440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:14.458917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:15.903223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:17.567442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:19.231737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:20.830573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:22.287839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.572703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:25.010687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:06.300740image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:07.892578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:09.542915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:11.236893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:12.966013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:14.571054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:16.041473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:17.705261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:19.362030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:20.951051image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:22.405871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.645533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:25.121811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:06.414620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:08.003090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:09.654069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:11.353200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:13.083183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:14.679397image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:16.157240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:17.821926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:19.469339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:21.046507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:22.501280image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.707455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:25.253019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:06.556813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:08.124479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:09.791248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:11.496177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:13.226098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:14.789824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:16.295150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:17.957882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:19.601721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:21.171723image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:22.619112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.829437image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:25.380020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:06.690344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:08.234557image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:09.922587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:11.636482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:13.410214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:14.900826image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:16.415968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:18.098944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:19.729205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:21.285153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:22.728829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.895375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:25.518441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:06.830269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:08.359195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:10.064293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:11.779662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:13.550029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:15.013551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:16.548013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:18.246812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:19.867079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:21.413492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:22.847465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.968625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:25.637853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:06.955221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:08.467495image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:10.186468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:11.906514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:13.677189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:15.110597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:16.666461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:18.369541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:19.989136image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:21.536004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:22.971477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:24.048916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:25.753433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:07.073900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:08.565892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:10.301648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:12.026078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:13.797478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:15.203748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:16.778590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:18.485712image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:20.106847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:21.653510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.093951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:24.130898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:25.825472image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:07.147385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:08.634391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:10.374823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:12.102062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:13.871230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:15.267409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:16.852936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:18.557795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:20.181834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:21.725856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.168204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:24.203078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:25.991961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:07.289220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:08.755261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:10.514485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:12.242604image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:14.014972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:15.382337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:16.980972image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:18.697980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:20.316955image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:21.845114image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:23.286231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T18:41:24.273096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-04T18:41:34.515200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
AQIAQI_BucketBenzeneCONH3NONO2NOxO3PM10PM2.5SO2TolueneXylene
AQI1.0000.4120.3360.4330.4870.3470.4520.4240.0740.8060.7770.3390.3300.131
AQI_Bucket0.4121.0000.0250.0700.1610.1410.1710.1720.1250.4260.3740.1160.1080.021
Benzene0.3360.0251.0000.3370.2830.2620.4130.376-0.0680.3620.3530.2380.8190.790
CO0.4330.0700.3371.0000.3540.3980.3730.445-0.1470.4540.4150.2450.3800.335
NH30.4870.1610.2830.3541.0000.2540.4220.3530.0000.4750.4680.2530.3520.044
NO0.3470.1410.2620.3980.2541.0000.4670.737-0.3400.3890.3850.1940.2290.188
NO20.4520.1710.4130.3730.4220.4671.0000.775-0.0790.4920.4720.2650.4320.250
NOx0.4240.1720.3760.4450.3530.7370.7751.000-0.2230.4720.4370.2370.3570.277
O30.0740.125-0.068-0.1470.000-0.340-0.079-0.2231.000-0.045-0.0380.069-0.080-0.032
PM100.8060.4260.3620.4540.4750.3890.4920.472-0.0451.0000.8740.3460.3420.072
PM2.50.7770.3740.3530.4150.4680.3850.4720.437-0.0380.8741.0000.2890.3120.155
SO20.3390.1160.2380.2450.2530.1940.2650.2370.0690.3460.2891.0000.3440.235
Toluene0.3300.1080.8190.3800.3520.2290.4320.357-0.0800.3420.3120.3441.0000.721
Xylene0.1310.0210.7900.3350.0440.1880.2500.277-0.0320.0720.1550.2350.7211.000

Missing values

2025-01-04T18:41:26.265100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-04T18:41:27.788620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-04T18:41:31.121018image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

StationIdDatetimePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
0AP0012017-11-24 17:00:0060.5098.002.3530.8018.258.500.111.85126.400.106.100.10NaNNaN
1AP0012017-11-24 18:00:0065.50111.252.7024.2015.079.770.113.17117.120.106.250.15NaNNaN
2AP0012017-11-24 19:00:0080.00132.002.1025.1815.1512.020.112.0898.980.205.980.18NaNNaN
3AP0012017-11-24 20:00:0081.50133.251.9516.2510.2311.580.110.47112.200.206.720.10NaNNaN
4AP0012017-11-24 21:00:0075.25116.001.4317.4810.4312.030.19.12106.350.205.750.08NaNNaN
5AP0012017-11-24 22:00:0069.25108.250.7018.4710.3813.800.19.2591.100.205.020.00NaNNaN
6AP0012017-11-24 23:00:0067.50111.501.0512.157.3017.650.19.40112.700.205.600.10NaNNaN
7AP0012017-11-25 00:00:0068.00111.001.2514.128.5020.280.18.90116.120.205.550.05NaNNaN
8AP0012017-11-25 01:00:0073.00102.000.3014.307.9011.500.311.80121.500.206.600.00NaNNaN
9AP0012017-11-25 02:00:0081.00123.000.8024.8513.8810.280.111.6283.800.236.770.10NaNNaN
StationIdDatetimePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
2589073WB0132020-06-30 15:00:0022.8236.759.8549.8659.7326.280.853.0832.381.406.82NaN59.0Satisfactory
2589074WB0132020-06-30 16:00:0023.0042.306.9532.7939.7223.680.706.2557.223.1811.62NaN59.0Satisfactory
2589075WB0132020-06-30 17:00:0017.7247.257.4226.5433.9525.610.788.8158.961.608.50NaN59.0Satisfactory
2589076WB0132020-06-30 18:00:0014.4542.429.3238.9148.2526.210.8134.8827.572.139.35NaN59.0Satisfactory
2589077WB0132020-06-30 19:00:0012.3544.888.2836.4444.7032.300.7823.9916.452.139.47NaN59.0Satisfactory
2589078WB0132020-06-30 20:00:0015.5547.807.2735.0842.3831.250.809.4017.242.5611.57NaN59.0Satisfactory
2589079WB0132020-06-30 21:00:0015.2342.306.1026.7832.8530.660.564.9117.463.4912.29NaN59.0Satisfactory
2589080WB0132020-06-30 22:00:0011.4040.956.5819.5326.1230.730.613.8117.241.838.88NaN59.0Satisfactory
2589081WB0132020-06-30 23:00:009.2534.339.1721.8531.0029.610.653.4412.741.408.43NaN59.0Satisfactory
2589082WB0132020-07-01 00:00:0010.5036.507.7822.5030.2527.230.582.8013.101.317.39NaN59.0Satisfactory